Professor: Edward Vytlacil

• Economics Ph.d., Univeristy of Chicago,
  • studied under James Heckman.
• Professor of Economics, Yale University,
  • previously on faculty at Stanford, Columbia, NYU.

Professor: Edward Vytlacil

• Field: Microeconometrics, Applied-Microeconometics;

  • Subfield: Causal Inference/treatment effects,
    
  • Applications: education, health, labor, corporate finance.

• Co-Editor of Journal of Applied Econometrics.

• Fellow of the Econometric Society.

• Founding member of the
  International Association of Applied Econometrics.

Professor: Edward Vytlacil

• Contact:
  • Email: edward.vytacil@yale.edu
  • Office Hours:
    • Location: 30 Hillhouse Ave, Room C149
    • Time: Mondays, 3-4:30pm.

Teaching Fellow: Ken Jung

• Yale Economics Ph.d. student.

• Fields:

  • Industrial Organization,
  • Environmental economics.

• Contact:

  • Email: ken.jung@yale.edu
  • Office Hours: TBA

Course: Econ 123

Second course in Economics dept sequence:
“Econometrics and Data Analysis”

• What is econometrics?
  • how is different from probability thoery? statistics?
    data science?
• Why “and Data Analysis”?

Probability theory

• Branch of mathematics,
• Derive implications of known probabilistic model.

Statistics builds upon prob. theory

• “The science of learning from data”
  (estimation and inference of probabilistic model),

Data Science

• If statistics is the “science of learning from data,”
  then what is data science?

• Is data science another name for applied statistics?

Data Science builds upon stats & CS

• Data science origins in:
  • CS/engineering (not math),
  • environment of data abundance.

Data Science builds upon stats & CS

• Data science different focus from stat, more focus on:
  • computation,
  • algorithms,
  • data visualization,
  • prediction,
  • domain expertise,
  • work flow…

Data Science builds upon stats & CS

• Data science instruction different, often
  • less theoretical,
  • more focus on coding,
  • more focus on experience of working with data,
  • inverting standard statistics and econometrics teaching pedagogy.

Econometrics

• Is econometrics the application of statistics to economic data? of data science to economic data?

Econometrics

Samuelson, Koopmans, and Stone (1954):

the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference

Econometrics builds upon stats and econ theory

• Merging of statistics, economic theory, and data
  (and more recently data science)

Econometrics builds upon stats and econ theory

• Focus on counterfactual prediction,
  • sometimes answering “why” and often “what if”?

Goals of econometrics include

• Understanding economic phenomenon,
  • e.g., why did female labor force participation increase dramatically, especially in professional fields?

Goals of econometrics include

Goals of econometrics include

Goals of econometrics include

• Estimate economically meaningful quantities,
  • e.g., supply and demand functions, hedonic equations.

Influence of Econometrics

• Econometrics grew out of econ, but has been influential in:
  • other social sciences
    (political science, law, …),
  • policy,
  • industry.
• Has influenced statistics and CS/AI,
  • though relationship often contentious.

Econ 117 & 123

Sequence in “Econometrics and Data Analysis’’

• An Econometrics sequence
  • Considers empirical applications of interest to economists;
  • Teaches how economists think about data, connecting data to economic models…

Econ 117 & 123

Sequence in “Econometrics and Data Analysis”

• An Econometrics sequence
  • Covers theory and methods from statistics and
    data science, but . . .
  • Focuses on methods developed in econometrics to answer questions of interest to economists,
    • For example, instrumental variables analysis,
      difference in difference methods.

Econ 117 & 123

Sequence in “Econometrics and Data Analysis”

• Why “and Data Analysis”?
  • inverted teaching pedagogy,
    
  • more focus on:
    • coding,
      
    • working with real data,
      
    • leveraging modern computing power,
      
    • work flow.

Goal of Econ 123

• By the time you complete this course, for you to have the strong basis for conducting original empirical research in
  • economics,
  • other social sciences
  • policy research,
  • industry.

Econ 123 builds upon Econ 117 by:

• Covering additional, advanced topics including:

  1. computational methods for infernece,
  2. instrumental variables,
     
  3. panel data methods,
     
  4. maximum likelihood estimation,
     
  5. nonlinear models, including limited dependent variable models.

Econ 123 builds upon Econ 117 by:

• Go deeper on workflow, from acquiring data to presenting results.

• Building upon the R coding you learned in Econ 117, e.g.,

  • User defined functions,
  • Apply family,
  • Simulation/Monte Carlo analysis.

R in Econ 123

• In this course, we will use R,
  
  • in R-Studio IDE, to

R in Econ 123

• create reproducible documents with R-Markdown/Quarto
  • combining text, code and results.

R in Econ 123

• In this course, we will use R,

  • what if you want to use STATA, python, . . .

• Advantages of R over other options? Disadvantages?

Econ 123 Applications include:

• Finance:
  1. Asset diversification,
  2. Capital Asset Pricing Model,
  3. Event studies.

Econ 123 Applications include:

• Discrimination, including in
  1. loans,
  2. job market,
  3. police force.

Will relate to economic models of discrimination:
statistical- vs taste-based discrimination.

Econ 123: Prerequisites

• Pre-Req:
  Econ 117: Introduction to Econometrics and Data Analysis.

• What if you haven’t taken Econ 117?

  • Econometrics is different from S&DS,
    • no close substitutea for Econ 117 in S&DS,
      
    • no close substitute for Econ 123 in S&DS.
  • You should take Econ 117!

Econ 123: Prerequisites

• Pre-Req:
  Econ 117: Introduction to Econometrics and Data Analysis.

• What if you haven’t taken Econ 117?

  • You should take Econ 117!

  • However, I will permit you to take Econ 123 if:

    • you have taken S&DS 220 or 230, or
    • with special permission.

Classes alternate lectures and labs:

• Lectures: Lecture slides will be posted on the course webpage, but are not designed as a substitute for attending lecture.

• Labs: In-class labs where we live-code in R to analyze real data. The labs will be designed to directly help you with your problem sets.

• You are expected to attend lectures and labs.
  I will call on students.

Course Webpages

• Canvas webpage https://yale.instructure.com/courses/83774
  • Includes schedule, lecture slides, lab materials,
    problem sets, readings and more.
    
  • Updated syllabus as we go.
• Ed Discussion webpage: https://edstem.org/us/courses/32196/discussion/
  • place to ask questions about the course,
    get help from staff and peers.

On-Line Quizzes:

• Posted on Fridays:
  • to the course webpage.
  • most weeks,
  • remain live for 48 hour once posted,
  • you have one hour to complete the quiz once you start it.
• Focus on theoretical questions, some questions on R coding.

On-Line Quizzes:

• Open book/open notes,
  • but you cannot collaborate with, or discuss with, other students until the solutions are posted.
• Lowest quiz score will be dropped.

Problem Sets

• Primarily empirical, based on academic research papers, though include some theoretical questions.

• Should be turned in following the same problem set submission guidelines that you used in Econ 117.

• Due dates are strict.¹

• The lowest problem set score will be dropped.

Problem Sets

• You may work in groups of up to four students on the problem sets.¹

• However, you must turn in your own assignment and indicate on your submission the other members of the group.

Exams

• Midterm: Open book/open notes
  • in-class, March 9 (date tentative)
• Final: Open book/open notes
  • Tuesday, May 9, 2023 at 2pm.
• focus on theory, with some R related questions including interpreting empirical output from R.

End of Term Empirical Project

• Address a research question of your choosing, applying the methods from this course to a relevant data set.
• You may work in groups of up to four students.

End of Term Empirical Project

• Deliverables:

  • The detailed research proposal, due on April 4.
  • Final term paper, approximately 15 to 25 pages long, due on May 10.

• Research Paper Guidelines

• Suggested Data Sources for Empirical Project

What’s next

• Motivating example: labor force participation of women, based on Goldin (2006b)

  • illustrate empirical work in economics, interplay between economic theory, econometrics, and data.

• Review causal inference, introduce models of discrimination.

• Review inference, with applications to causal effects and discrimination.

What’s next

• By next Tuesday, review
  • Handout 1: Rules for Expected Value and Variance
  • Handout 2: Binomial, Normal and t- Distributions
  • Optional Reading:
    • Hogg, Tanis, and Zimmerman (2020) 2.2-2.4, 3.3, 4.2, 5.5
    • Wooldridge (2020) B-3, B-4a-B-4d, B-5a-B-5e
    • Hands-On Programming with R by Garrett Grolemund.

What’s next

• First quiz: Friday January 27
  • covering material from Handouts 1 and 2, and material on inference.
• First problem set assigned Thursday January 26.